Downloads provided by UsageCountsArtificial neural network model to predict student performance using nonpersonal information
Copyright policy ) Heyul Chavez; Heyul Chavez; Bill Chavez-Arias; Sebastian Contreras-Rosas; Jose María Alvarez-Rodríguez; Carlos Raymundo;
Artificial neural network model to predict student performance using nonpersonal information
In recent years, artificial intelligence has played an important role in education, wherein one of the most commonly used applications is forecasting students’ academic performance based on personal information such as social status, income, address, etc. This study proposes and develops an artificial neural network model capable of determining whether a student will pass a certain class without using personal or sensitive information that may compromise student privacy. For model training, we used information regarding 32,000 students collected from The Open University of the United Kingdom, such as number of times they took the course, average number of evaluations, course pass rate, average use of virtual materials per date and number of clicks in virtual classrooms. Attributes selected for the model are as follows: 93.81% accuracy, 94.15% precision, 95.13% recall, and 94.64% F1-score. These results will help the student authorities to take measures to avoid withdrawal and underachievement.
- Carlos III University of Madrid Spain
- Auckland University of Technology New Zealand
- KU Leuven Belgium
- King Juan Carlos University Spain
- KU Leuven Belgium
Artificial neural network, Artificial intelligence, Effectiveness of Intelligent Tutoring Systems, Class (philosophy), Course pass rate, personal data, Social Sciences, forecasting, privacy, Artificial neural network model, Education, Data from The Open University, Number of course attempts, Model performance metrics, Sociology, Artificial Intelligence, Computer security, Personally identifiable information, Cognitive psychology, Machine learning, Psychology, Student Modeling, L7-991, Educational Data Mining, Informática, Transformative Potential of Blended Learning in Education, Data-driven Education, academic performance, Education (General), Intelligent Tutoring Systems, neural networks, Social science, Computer science, Privacy considerations, Mathematics education, Computer Science Applications, FOS: Sociology, FOS: Psychology, Compromise, Use of virtual materials, Computer Science, Physical Sciences, Student Performance Prediction, Recall, Educational Data Mining and Learning Analytics, Student performance forecasting
Artificial neural network, Artificial intelligence, Effectiveness of Intelligent Tutoring Systems, Class (philosophy), Course pass rate, personal data, Social Sciences, forecasting, privacy, Artificial neural network model, Education, Data from The Open University, Number of course attempts, Model performance metrics, Sociology, Artificial Intelligence, Computer security, Personally identifiable information, Cognitive psychology, Machine learning, Psychology, Student Modeling, L7-991, Educational Data Mining, Informática, Transformative Potential of Blended Learning in Education, Data-driven Education, academic performance, Education (General), Intelligent Tutoring Systems, neural networks, Social science, Computer science, Privacy considerations, Mathematics education, Computer Science Applications, FOS: Sociology, FOS: Psychology, Compromise, Use of virtual materials, Computer Science, Physical Sciences, Student Performance Prediction, Recall, Educational Data Mining and Learning Analytics, Student performance forecasting
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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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